Research Papers:

Using molecular functional networks to manifest connections between obesity and obesity-related diseases

Jialiang Yang, Jing Qiu, Kejing Wang, Lijuan Zhu, Jingjing Fan, Deyin Zheng, Xiaodi Meng, Jiasheng Yang, Lihong Peng, Yu Fu, Dahan Zhang, Shouneng Peng, Haiyun Huang and Yi Zhang _

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Oncotarget. 2017; 8:85136-85149. https://doi.org/10.18632/oncotarget.19490

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Jialiang Yang1, Jing Qiu2, Kejing Wang2, Lijuan Zhu2, Jingjing Fan2, Deyin Zheng3, Xiaodi Meng4, Jiasheng Yang5, Lihong Peng1, Yu Fu2, Dahan Zhang6, Shouneng Peng7, Haiyun Huang2 and Yi Zhang2

1College of Information Engineering, Changsha Medical University, Changsha 410219, P. R. China

2Department of Mathematics/Network Engineering/Bioscience and Bioengineering/Library, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China

3Department of Mathematics, Hangzhou Normal University, Hangzhou 311121, P. R. China

4Department of Food Science, Fujian Agriculture and Forestry University, Fuzhou 35002, P. R. China

5Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore

6Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, P. R. China

7Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

Correspondence to:

Yi Zhang, email: [email protected]

Haiyun Huang, email: [email protected]

Keywords: bioinformatics, human obesity, obesity-related diseases, protein interaction network, gene expression

Received: May 03, 2017    Accepted: June 05, 2017    Published: July 22, 2017


Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.

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